MOTCOM: The Multi-Object Tracking Dataset Complexity Metric

Malte Pedersen; Joakim Bruslund Haurum; Patrick Dendorfer; Thomas B. Moeslund

doi:10.1007/978-3-031-20074-8_2

MOTCOM: The Multi-Object Tracking Dataset Complexity Metric

Malte Pedersen^*, Joakim Bruslund Haurum, Patrick Dendorfer, Thomas B. Moeslund

^*Kontaktforfatter

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

2 Citationer (Scopus)

Abstract

There exists no comprehensive metric for describing the complexity of Multi-Object Tracking (MOT) sequences. This lack of metrics decreases explainability, complicates comparison of datasets, and reduces the conversation on tracker performance to a matter of leader board position. As a remedy, we present the novel MOT dataset complexity metric (MOTCOM), which is a combination of three sub-metrics inspired by key problems in MOT: occlusion, erratic motion, and visual similarity. The insights of MOTCOM can open nuanced discussions on tracker performance and may lead to a wider acknowledgement of novel contributions developed for either less known datasets or those aimed at solving sub-problems.
We evaluate MOTCOM on the comprehensive MOT17, MOT20, and MOTSynth datasets and show that MOTCOM is far better at describing the complexity of MOT sequences compared to the conventional density and number of tracks. Project page at https://vap.aau.dk/motcom

Originalsprog	Engelsk
Titel	Computer Vision –- ECCV 2022 : 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part VIII
Redaktører	Shai Avidan, Gabriel Brostow, Moustapha Cissé, Giovanni Maria Farinella, Tal Hassner
Forlag	Springer
Publikationsdato	12 nov. 2022
Sider	20-37
ISBN (Trykt)	978-3-031-20073-1
ISBN (Elektronisk)	978-3-031-20074-8
DOI	https://doi.org/10.1007/978-3-031-20074-8_2
Status	Udgivet - 12 nov. 2022
Begivenhed	European Conference on Computer Vision 2022 - Tel Aviv, Israel Varighed: 23 okt. 2022 → 27 okt. 2022 https://eccv2022.ecva.net/

Konference

Konference	European Conference on Computer Vision 2022
Land/Område	Israel
By	Tel Aviv
Periode	23/10/2022 → 27/10/2022
Internetadresse	https://eccv2022.ecva.net/

Navn	Lecture Notes in Computer Science (LNCS)
Vol/bind	13668
ISSN	0302-9743

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10.1007/978-3-031-20074-8_2

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Citationsformater

Pedersen, M., Haurum, J. B., Dendorfer, P., & Moeslund, T. B. (2022). MOTCOM: The Multi-Object Tracking Dataset Complexity Metric. I S. Avidan, G. Brostow, M. Cissé, G. M. Farinella, & T. Hassner (red.), Computer Vision –- ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part VIII (s. 20-37). Springer. https://doi.org/10.1007/978-3-031-20074-8_2

Pedersen, Malte ; Haurum, Joakim Bruslund ; Dendorfer, Patrick et al. / MOTCOM: The Multi-Object Tracking Dataset Complexity Metric. Computer Vision –- ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part VIII. red. / Shai Avidan ; Gabriel Brostow ; Moustapha Cissé ; Giovanni Maria Farinella ; Tal Hassner. Springer, 2022. s. 20-37 (Lecture Notes in Computer Science (LNCS), Bind 13668).

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title = "MOTCOM: The Multi-Object Tracking Dataset Complexity Metric",

abstract = "There exists no comprehensive metric for describing the complexity of Multi-Object Tracking (MOT) sequences. This lack of metrics decreases explainability, complicates comparison of datasets, and reduces the conversation on tracker performance to a matter of leader board position. As a remedy, we present the novel MOT dataset complexity metric (MOTCOM), which is a combination of three sub-metrics inspired by key problems in MOT: occlusion, erratic motion, and visual similarity. The insights of MOTCOM can open nuanced discussions on tracker performance and may lead to a wider acknowledgement of novel contributions developed for either less known datasets or those aimed at solving sub-problems.We evaluate MOTCOM on the comprehensive MOT17, MOT20, and MOTSynth datasets and show that MOTCOM is far better at describing the complexity of MOT sequences compared to the conventional density and number of tracks. Project page at https://vap.aau.dk/motcom",

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Pedersen, M , Haurum, JB, Dendorfer, P & Moeslund, TB 2022, MOTCOM: The Multi-Object Tracking Dataset Complexity Metric. i S Avidan, G Brostow, M Cissé, GM Farinella & T Hassner (red), Computer Vision –- ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part VIII. Springer, Lecture Notes in Computer Science (LNCS), bind 13668, s. 20-37, European Conference on Computer Vision 2022, Tel Aviv, Israel, 23/10/2022. https://doi.org/10.1007/978-3-031-20074-8_2

MOTCOM: The Multi-Object Tracking Dataset Complexity Metric. / Pedersen, Malte ; Haurum, Joakim Bruslund; Dendorfer, Patrick et al.
Computer Vision –- ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part VIII. red. / Shai Avidan; Gabriel Brostow; Moustapha Cissé; Giovanni Maria Farinella; Tal Hassner. Springer, 2022. s. 20-37 (Lecture Notes in Computer Science (LNCS), Bind 13668).

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

TY - GEN

T1 - MOTCOM: The Multi-Object Tracking Dataset Complexity Metric

AU - Pedersen, Malte

AU - Haurum, Joakim Bruslund

AU - Dendorfer, Patrick

AU - Moeslund, Thomas B.

PY - 2022/11/12

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N2 - There exists no comprehensive metric for describing the complexity of Multi-Object Tracking (MOT) sequences. This lack of metrics decreases explainability, complicates comparison of datasets, and reduces the conversation on tracker performance to a matter of leader board position. As a remedy, we present the novel MOT dataset complexity metric (MOTCOM), which is a combination of three sub-metrics inspired by key problems in MOT: occlusion, erratic motion, and visual similarity. The insights of MOTCOM can open nuanced discussions on tracker performance and may lead to a wider acknowledgement of novel contributions developed for either less known datasets or those aimed at solving sub-problems.We evaluate MOTCOM on the comprehensive MOT17, MOT20, and MOTSynth datasets and show that MOTCOM is far better at describing the complexity of MOT sequences compared to the conventional density and number of tracks. Project page at https://vap.aau.dk/motcom

AB - There exists no comprehensive metric for describing the complexity of Multi-Object Tracking (MOT) sequences. This lack of metrics decreases explainability, complicates comparison of datasets, and reduces the conversation on tracker performance to a matter of leader board position. As a remedy, we present the novel MOT dataset complexity metric (MOTCOM), which is a combination of three sub-metrics inspired by key problems in MOT: occlusion, erratic motion, and visual similarity. The insights of MOTCOM can open nuanced discussions on tracker performance and may lead to a wider acknowledgement of novel contributions developed for either less known datasets or those aimed at solving sub-problems.We evaluate MOTCOM on the comprehensive MOT17, MOT20, and MOTSynth datasets and show that MOTCOM is far better at describing the complexity of MOT sequences compared to the conventional density and number of tracks. Project page at https://vap.aau.dk/motcom

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A2 - Avidan, Shai

A2 - Brostow, Gabriel

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ER -

Pedersen M , Haurum JB, Dendorfer P, Moeslund TB. MOTCOM: The Multi-Object Tracking Dataset Complexity Metric. I Avidan S, Brostow G, Cissé M, Farinella GM, Hassner T, red., Computer Vision –- ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part VIII. Springer. 2022. s. 20-37. (Lecture Notes in Computer Science (LNCS), Bind 13668). doi: 10.1007/978-3-031-20074-8_2

MOTCOM: The Multi-Object Tracking Dataset Complexity Metric

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